What are Software Developer metrics? Crafting the perfect Software Developer metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find Software Developer metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Software Developer metrics and KPIs 1. defect density Defect density measures the number of defects per unit of software size, usually per thousand lines of code (KLOC)
What good looks like for this metric: 1-5 defects per KLOC
Ideas to improve this metric Improve code reviews Implement automated testing Enhance developer training Increase test coverage Use static code analysis 2. code coverage Code coverage measures the percentage of code that is executed by automated tests
What good looks like for this metric: 70-80%
Ideas to improve this metric Write more unit tests Implement integration testing Use better testing tools Collaborate closely with QA team Regularly refactor code for testability 3. mean time to resolve (MTTR) MTTR measures the average time taken to resolve a defect once it has been identified
What good looks like for this metric: Less than 8 hours
Ideas to improve this metric Streamline incident management process Automate triage tasks Improve defect prioritization Enhance developer expertise Implement rapid feedback loops 4. customer-reported defects This metric counts the number of defects reported by end users or customers
What good looks like for this metric: Less than 1 defect per month
Ideas to improve this metric Implement thorough user acceptance testing Conduct regular beta tests Enhance support and issue tracking Improve customer feedback channels Use user personas in development 5. code churn Code churn measures the amount of code changes over a period of time, indicating stability and code quality
What good looks like for this metric: 10-20%
Ideas to improve this metric Encourage smaller, iterative changes Implement continuous integration Use version control effectively Conduct regular code reviews Enhance change management processes
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1. Defect Density Measures the number of defects per unit size of the software, usually per thousand lines of code
What good looks like for this metric: 1-10 defects per KLOC
Ideas to improve this metric Implement code reviews Increase automated testing Enhance developer training Use static code analysis tools Adopt Test-Driven Development (TDD) 2. Mean Time to Failure (MTTF) Measures the average time between failures for a system or component during operation
What good looks like for this metric: Varies widely by industry and system type, generally higher is better
Ideas to improve this metric Conduct regular maintenance routines Implement rigorous testing cycles Enhance monitoring and alerting systems Utilise redundancy and failover mechanisms Improve codebase documentation 3. Customer-Reported Incidents Counts the number of issues or bugs reported by customers within a given period
What good looks like for this metric: Varies depending on product and customer base, generally lower is better
Ideas to improve this metric Engage in proactive customer support Release regular updates and patches Conduct user feedback sessions Improve user documentation Monitor and analyse incident trends 4. Code Coverage Indicates the percentage of the source code covered by automated tests
What good looks like for this metric: 70-90% code coverage
Ideas to improve this metric Increase unit testing Use automated testing tools Adopt continuous integration practices Refactor legacy code Integrate end-to-end testing 5. Release Frequency Measures how often new releases are deployed to production
What good looks like for this metric: Depends on product and development cycle; frequently updated software is often more reliable
Ideas to improve this metric Adopt continuous delivery Automate deployment processes Improve release planning Reduce deployment complexity Engage in regular sprint retrospectives
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1. Code Quality Measures the standards of the code written by the developer using metrics like cyclomatic complexity, code churn, and code maintainability index
What good looks like for this metric: Maintainability index above 70
Ideas to improve this metric Conduct regular code reviews Utilise static code analysis tools Adopt coding standards and guidelines Refactor code regularly to reduce complexity Invest in continuous learning and training 2. Deployment Frequency Evaluates the frequency at which a developer releases code changes to production
What good looks like for this metric: Multiple releases per week
Ideas to improve this metric Automate deployment processes Use continuous integration and delivery pipelines Schedule regular release sessions Encourage modular code development Enhance collaboration with DevOps teams 3. Lead Time for Changes Measures the time taken from code commit to deployment in production, reflecting efficiency in development and delivery
What good looks like for this metric: Less than one day
Ideas to improve this metric Streamline the code review process Optimise testing procedures Improve communication across teams Automate build and testing workflows Implement parallel development tracks 4. Change Failure Rate Represents the proportion of deployments that result in a failure requiring a rollback or hotfix
What good looks like for this metric: Less than 15%
Ideas to improve this metric Implement thorough testing before deployment Decrease batch size of code changes Conduct post-implementation reviews Improve error monitoring and logging Enhance rollback procedures 5. System Downtime Assesses the total time that applications are non-operational due to code changes or failures attributed to backend systems
What good looks like for this metric: Less than 0.1% downtime
Ideas to improve this metric Invest in high availability infrastructure Enhance real-time monitoring systems Regularly test system resilience Implement effective incident response plans Improve software redundancy mechanisms
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1. Code Quality Measures the frequency and severity of bugs detected in the codebase.
What good looks like for this metric: Less than 10 bugs per 1000 lines of code
Ideas to improve this metric Implement regular code reviews Use static code analysis tools Provide training on best coding practices Encourage test-driven development Adopt a peer programming strategy 2. Deployment Frequency Tracks how often code changes are successfully deployed to production.
What good looks like for this metric: Deploy at least once a day
Ideas to improve this metric Automate the deployment pipeline Reduce bottlenecks in the process Regularly publish small, manageable changes Incentivise swift yet comprehensive testing Improve team communication and collaboration 3. Mean Time to Recovery (MTTR) Measures the average time taken to recover from a service failure.
What good looks like for this metric: Less than 1 hour
Ideas to improve this metric Develop a robust incident response plan Streamline rollback and recovery processes Use monitoring tools to detect issues early Conduct post-mortems and learn from failures Enhance system redundancy and fault tolerance 4. Test Coverage Represents the percentage of code which is tested by automated tests.
What good looks like for this metric: 70% to 90%
Ideas to improve this metric Implement continuous integration with testing Educate developers on writing effective tests Regularly update and refactor out-of-date tests Encourage a culture of writing tests Utilise behaviour-driven development techniques 5. API Response Time Measures the time taken for an API to respond to a request.
What good looks like for this metric: Less than 200ms
Ideas to improve this metric Optimize database queries Utilise caching effectively Reduce payload size Use load balancing techniques Profile and identify performance bottlenecks
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1. Feature Completion Rate The percentage of features fully implemented and functional compared to the initial plan
What good looks like for this metric: 80% to 100% during development cycle
Ideas to improve this metric Improve project management processes Ensure clear feature specifications Allocate adequate resources Conduct regular progress reviews Increase team collaboration 2. Planned vs. Actual Features The ratio of features planned to features actually completed
What good looks like for this metric: Equal or close to 1:1
Ideas to improve this metric Create realistic project plans Regularly update feature lists Adjust deadlines as needed Align teams on priorities Open channels for feedback 3. Feature Review Score Average score from review sessions that evaluate feature completion and quality
What good looks like for this metric: Scores above 8 out of 10
Ideas to improve this metric Provide detailed review criteria Use peer review strategies Incorporate customer feedback Holistic testing methodologies Re-evaluate low scoring features 4. Feature Dependency Resolution Time Average time taken to resolve issues linked to feature dependencies
What good looks like for this metric: Resolution time within 2 weeks
Ideas to improve this metric Map feature dependencies early Optimize dependency workflow Increase team communication Utilise dependency management tools Prioritize complex dependencies 5. Change Request Frequency Number of changes requested post-initial feature specification
What good looks like for this metric: Less than 10% of total features
Ideas to improve this metric Ensure initial feature clarity Involve stakeholders early on Implement change control processes Clarify project scope Encourage proactive team discussions
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1. Code Coverage Measures the percentage of your code that is covered by automated tests
What good looks like for this metric: 70%-90%
Ideas to improve this metric Increase unit tests Use code coverage tools Refactor complex code Implement test-driven development Conduct code reviews frequently 2. Code Complexity Assesses the complexity of the code using metrics like Cyclomatic Complexity
What good looks like for this metric: 1-10 (Lower is better)
Ideas to improve this metric Simplify conditional statements Refactor to smaller functions Reduce nested loops Use design patterns appropriately Perform regular code reviews 3. Technical Debt Measures the cost of additional work caused by choosing easy solutions now instead of better approaches
What good looks like for this metric: Less than 5%
Ideas to improve this metric Refactor code regularly Avoid quick fixes Ensure high-quality code reviews Update and follow coding standards Use static code analysis tools 4. Defect Density Calculates the number of defects per 1000 lines of code
What good looks like for this metric: Less than 1 defect/KLOC
Ideas to improve this metric Implement thorough testing Increase peer code reviews Enhance developer training Use static analysis tools Adopt continuous integration 5. Code Churn Measures the amount of code that is added, modified, or deleted over time
What good looks like for this metric: 10-20%
Ideas to improve this metric Stabilise project requirements Improve initial code quality Adopt pair programming Reduce unnecessary refactoring Enhance documentation
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1. Feature Implementation Ratio The ratio of implemented features to planned features.
What good looks like for this metric: 80-90%
Ideas to improve this metric Prioritise features based on user impact Allocate dedicated resources for feature development Conduct regular progress reviews Utilise agile methodologies for iteration Ensure clear feature specifications 2. User Acceptance Test Pass Rate Percentage of features passing user acceptance testing.
What good looks like for this metric: 95%+
Ideas to improve this metric Enhance test case design Involve users early in the testing process Provide comprehensive user training Utilise automated testing tools Identify and fix defects promptly 3. Bug Resolution Time Average time taken to resolve bugs during feature development.
What good looks like for this metric: 24-48 hours
Ideas to improve this metric Implement a robust issue tracking system Prioritise critical bugs Conduct regular team stand-ups Improve cross-functional collaboration Establish a swift feedback loop 4. Code Quality Index Assessment of code quality using a standard index or score.
What good looks like for this metric: 75-85%
Ideas to improve this metric Conduct regular code reviews Utilise static code analysis tools Refactor code periodically Strictly adhere to coding standards Invest in developer training 5. Feature Usage Frequency Frequency at which newly implemented features are used.
What good looks like for this metric: 70%+ usage of released features
Ideas to improve this metric Enhance user interface design Provide user guides or tutorials Gather user feedback on new features Offer feature usage incentives Regularly monitor usage statistics
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1. Deployment Frequency Measures how often new updates are deployed to production
What good looks like for this metric: Once per week
Ideas to improve this metric Automate deployment processes Implement continuous integration Use feature toggles Practice trunk-based development Reduce batch sizes 2. Lead Time for Changes Time taken from code commit to deployment in production
What good looks like for this metric: One day to one week
Ideas to improve this metric Improve code review process Minimise work in progress Optimise build processes Automate testing pipelines Implement parallel builds 3. Mean Time to Recovery Time taken to recover from production failures
What good looks like for this metric: Less than one hour
Ideas to improve this metric Implement robust monitoring tools Create a clear incident response plan Use canary releases Conduct regular disaster recovery drills Enhance rollback procedures 4. Change Failure Rate Percentage of changes that result in production failures
What good looks like for this metric: Less than 15%
Ideas to improve this metric Increase test coverage Perform thorough code reviews Conduct root cause analysis Use static code analysis tools Implement infrastructure as code 5. Cycle Time Time to complete one development cycle from start to finish
What good looks like for this metric: Two weeks
Ideas to improve this metric Adopt agile methodologies Limit work in progress Use time-boxed sprints Continuously prioritise tasks Improve collaboration among teams
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1. Vulnerability Density Measures the number of vulnerabilities per thousand lines of code. It helps to identify vulnerable areas in the codebase that need attention.
What good looks like for this metric: 0-1 vulnerabilities per KLOC
Ideas to improve this metric Conduct regular code reviews Use static analysis tools Implement secure coding practices Provide security training for developers Perform security-focused testing 2. Mean Time to Resolve Vulnerabilities (MTTR) The average time it takes to resolve vulnerabilities from the time they are identified.
What good looks like for this metric: Less than 30 days
Ideas to improve this metric Prioritise vulnerabilities based on severity Automate vulnerability management processes Allocate dedicated resources for vulnerability remediation Establish a clear vulnerability response process Regularly monitor and report on MTTR 3. Percentage of Code Covered by Security Testing The proportion of the codebase that is covered by security tests, helping to ensure code is thoroughly tested for vulnerabilities.
What good looks like for this metric: 90% or higher
Ideas to improve this metric Increase the frequency of security tests Use automated security testing tools Integrate security tests into the CI/CD pipeline Regularly update and expand test cases Provide training on writing effective security tests 4. Number of Security Incidents The total count of security incidents, including breaches, detected within a given period.
What good looks like for this metric: Zero incidents
Ideas to improve this metric Implement continuous monitoring Conduct regular penetration testing Deploy intrusion detection systems Educate employees on security best practices Establish a strong incident response plan 5. False Positive Rate of Security Tools The percentage of security alerts that are not true threats, which can lead to resource wastage and alert fatigue.
What good looks like for this metric: Less than 5%
Ideas to improve this metric Regularly update security tool configurations Train security teams to properly interpret alerts Use machine learning to improve tool accuracy Combine multiple security tools for better context Implement regular reviews of alerts to refine rules
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1. Release Frequency Measures the number of releases over a specific period. Indicates how quickly updates are being deployed.
What good looks like for this metric: 1-2 releases per month
Ideas to improve this metric Automate deployment processes Implement continuous integration/continuous deployment practices Invest in developer training Regularly review and optimise code Deploy smaller, incremental updates 2. Lead Time for Changes The average time it takes from code commitment to production release. Reflects the efficiency of the development pipeline.
What good looks like for this metric: Less than one week
Ideas to improve this metric Streamline workflow processes Use automated testing tools Enhance code review efficiency Implement Kanban or Agile methodologies Identify and eliminate bottlenecks 3. Change Failure Rate Percentage of releases that cause a failure in production. Indicates the reliability of releases.
What good looks like for this metric: Less than 15%
Ideas to improve this metric Increase testing coverage Conduct thorough code reviews Implement feature flags Improve rollback procedures Provide better training for developers 4. Mean Time to Recovery (MTTR) Average time taken to recover from a failure. Reflects the team's ability to handle incidents.
What good looks like for this metric: Less than one hour
Ideas to improve this metric Establish clear incident response protocols Automate recovery processes Enhance monitoring and alerts Regularly conduct disaster recovery drills Analyse incidents post-mortem to prevent recurrence 5. Number of Bugs Found Post-Release The count of bugs discovered by users post-release. Indicates the quality of software before deployment.
What good looks like for this metric: Fewer than 5 bugs per release
Ideas to improve this metric Enhance pre-release testing Implement user acceptance testing Increase use of beta testing Utilise static code analysis tools Improve requirement gathering and planning
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Tracking your Software Developer metrics Having a plan is one thing, sticking to it is another.
Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
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